Book Image

Amazon SageMaker Best Practices

By : Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode
Book Image

Amazon SageMaker Best Practices

By: Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode

Overview of this book

Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows.
Table of Contents (20 chapters)
Section 1: Processing Data at Scale
Section 2: Model Training Challenges
Section 3: Manage and Monitor Models
Section 4: Automate and Operationalize Machine Learning

Amazon SageMaker Debugger essentials

In this section, you will learn about the basic terminology and capabilities of Amazon SageMaker Debugger. Using Debugger with your training jobs involves three high-level steps:  

  1. Configuring the training job to use SageMaker Debugger.
  2. Analyzing the collected tensors and metrics.
  3. Taking action.

The preceding points are illustrated in the following diagram:

Figure 7.1 – Amazon SageMaker Debugger overview

As we dive into each one of these steps, we will introduce the necessary terminology.

Configuring a training job to use SageMaker Debugger

The first step is to configure training jobs to use Amazon SageMaker Debugger. By now, you are familiar with using the Estimator object from SageMaker SDK to launch training jobs. To use Amazon SageMaker Debugger, you must enhance Estimator with three additional configuration parameters: DebuggerHookConfig, Rules, and ProfilerConfig.